import argparse
import logging
import os
import re

import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    DataCollatorForLanguageModeling,
    PushToHubCallback,
    TFAutoModelForMaskedLM,
    create_optimizer,
)


_SCREAMING_SNAKE_CASE 		=      logging.getLogger(__name__)

_SCREAMING_SNAKE_CASE 		=      tf.data.AUTOTUNE





def 			lowercase(	)		->	Any:







 '''simple docstring'''
 UpperCamelCase			  =      argparse.ArgumentParser(description="""Train a masked language model on TPU."""       )
 parser.add_argument(
     """--pretrained_model_config"""							,					type=UpperCamelCase_							,					default="""roberta-base"""							,					help="""The model config to use. Note that we don't copy the model's weights, only the config!"""							,					)
 parser.add_argument(
     """--tokenizer"""							,					type=UpperCamelCase_							,					default="""unigram-tokenizer-wikitext"""							,					help="""The name of the tokenizer to load. We use the pretrained tokenizer to initialize the model's vocab size."""							,					)

 parser.add_argument(
     """--per_replica_batch_size"""							,					type=UpperCamelCase_							,					default=8							,					help="""Batch size per TPU core."""							,					)

 parser.add_argument(
     """--no_tpu"""							,					action="""store_true"""							,					help="""If set, run on CPU and don't try to initialize a TPU. Useful for debugging on non-TPU instances."""							,					)

 parser.add_argument(
     """--tpu_name"""							,					type=UpperCamelCase_							,					help="""Name of TPU resource to initialize. Should be blank on Colab, and 'local' on TPU VMs."""							,					default="""local"""							,					)

 parser.add_argument(
     """--tpu_zone"""							,					type=UpperCamelCase_							,					help="""Google cloud zone that TPU resource is located in. Only used for non-Colab TPU nodes."""							,					)

 parser.add_argument(
     """--gcp_project"""							,					type=UpperCamelCase_							,					help="""Google cloud project name. Only used for non-Colab TPU nodes."""       )

 parser.add_argument(
     """--bfloat16"""							,					action="""store_true"""							,					help="""Use mixed-precision bfloat16 for training. This is the recommended lower-precision format for TPU."""							,					)

 parser.add_argument(
     """--train_dataset"""							,					type=UpperCamelCase_							,					help="""Path to training dataset to load. If the path begins with `gs://`"""
     """ then the dataset will be loaded from a Google Cloud Storage bucket."""							,					)

 parser.add_argument(
     """--shuffle_buffer_size"""							,					type=UpperCamelCase_							,					default=2**18							,					help="""Size of the shuffle buffer (in samples)"""							,					)

 parser.add_argument(
     """--eval_dataset"""							,					type=UpperCamelCase_							,					help="""Path to evaluation dataset to load. If the path begins with `gs://`"""
     """ then the dataset will be loaded from a Google Cloud Storage bucket."""							,					)

 parser.add_argument(
     """--num_epochs"""							,					type=UpperCamelCase_							,					default=1							,					help="""Number of epochs to train for."""							,					)

 parser.add_argument(
     """--learning_rate"""							,					type=UpperCamelCase_							,					default=1E-4							,					help="""Learning rate to use for training."""							,					)

 parser.add_argument(
     """--weight_decay_rate"""							,					type=UpperCamelCase_							,					default=1E-3							,					help="""Weight decay rate to use for training."""							,					)

 parser.add_argument(
     """--max_length"""							,					type=UpperCamelCase_							,					default=512							,					help="""Maximum length of tokenized sequences. Should match the setting used in prepare_tfrecord_shards.py"""							,					)

 parser.add_argument(
     """--mlm_probability"""							,					type=UpperCamelCase_							,					default=0.1_5							,					help="""Fraction of tokens to mask during training."""							,					)

 parser.add_argument("""--output_dir"""							,					type=UpperCamelCase_							,					required=UpperCamelCase_							,					help="""Path to save model checkpoints to."""       )
 parser.add_argument("""--hub_model_id"""							,					type=UpperCamelCase_							,					help="""Model ID to upload to on the Hugging Face Hub."""       )

 UpperCamelCase			  =      parser.parse_args()
 return args





def 			lowercase(	UpperCamelCase_       )		->	Dict:







 '''simple docstring'''
 try:
  if args.tpu_name:
   UpperCamelCase			  =      tf.distribute.cluster_resolver.TPUClusterResolver(
       args.tpu_name							,					zone=args.tpu_zone							,					project=args.gcp_project       )
  else:
   UpperCamelCase			  =      tf.distribute.cluster_resolver.TPUClusterResolver()
 except ValueError:
  raise RuntimeError(
      """Couldn't connect to TPU! Most likely you need to specify --tpu_name, --tpu_zone, or """
      """--gcp_project. When running on a TPU VM, use --tpu_name local."""       )

 tf.config.experimental_connect_to_cluster(UpperCamelCase_       )
 tf.tpu.experimental.initialize_tpu_system(UpperCamelCase_       )

 return tpu





def 			lowercase(	UpperCamelCase_       )		->	Dict:







 '''simple docstring'''
 UpperCamelCase			  =      0
 for file in file_list:
  UpperCamelCase			  =      file.split("""/"""       )[-1]
  UpperCamelCase			  =      re.search(R"""-\d+-(\d+)\.tfrecord"""							,					UpperCamelCase_       ).group(1       )
  UpperCamelCase			  =      int(UpperCamelCase_       )
  num_samples += sample_count

 return num_samples





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_=None       )		->	Any:







 '''simple docstring'''
 UpperCamelCase			  =      count_samples(UpperCamelCase_       )
 UpperCamelCase			  =      tf.data.Dataset.from_tensor_slices(UpperCamelCase_       )
 if shuffle:
  UpperCamelCase			  =      dataset.shuffle(len(UpperCamelCase_       )       )
 UpperCamelCase			  =      tf.data.TFRecordDataset(UpperCamelCase_							,					num_parallel_reads=UpperCamelCase_       )
 # TF can't infer the total sample count because it doesn't read all the records yet, so we assert it here
 UpperCamelCase			  =      dataset.apply(tf.data.experimental.assert_cardinality(UpperCamelCase_       )       )
 UpperCamelCase			  =      dataset.map(UpperCamelCase_							,					num_parallel_calls=UpperCamelCase_       )
 if shuffle:
  assert shuffle_buffer_size is not None
  UpperCamelCase			  =      dataset.shuffle(args.shuffle_buffer_size       )
 UpperCamelCase			  =      dataset.batch(UpperCamelCase_							,					drop_remainder=UpperCamelCase_       )
 UpperCamelCase			  =      dataset.map(UpperCamelCase_							,					num_parallel_calls=UpperCamelCase_       )
 UpperCamelCase			  =      dataset.prefetch(UpperCamelCase_       )
 return dataset





def 			lowercase(	UpperCamelCase_       )		->	str:







 '''simple docstring'''
 if not args.no_tpu:
  UpperCamelCase			  =      initialize_tpu(UpperCamelCase_       )
  UpperCamelCase			  =      tf.distribute.TPUStrategy(UpperCamelCase_       )
 else:
  UpperCamelCase			  =      tf.distribute.OneDeviceStrategy(device="""/gpu:0"""       )

 if args.bfloataa:
  tf.keras.mixed_precision.set_global_policy("""mixed_bfloat16"""       )

 UpperCamelCase			  =      AutoTokenizer.from_pretrained(args.tokenizer       )
 UpperCamelCase			  =      AutoConfig.from_pretrained(args.pretrained_model_config       )
 UpperCamelCase			  =      tokenizer.vocab_size

 UpperCamelCase			  =      tf.io.gfile.glob(os.path.join(args.train_dataset							,					"""*.tfrecord"""       )       )
 if not training_records:
  raise ValueError(f"""No .tfrecord files found in {args.train_dataset}."""       )
 UpperCamelCase			  =      tf.io.gfile.glob(os.path.join(args.eval_dataset							,					"""*.tfrecord"""       )       )
 if not eval_records:
  raise ValueError(f"""No .tfrecord files found in {args.eval_dataset}."""       )

 UpperCamelCase			  =      count_samples(UpperCamelCase_       )

 UpperCamelCase			  =      num_train_samples // (args.per_replica_batch_size * strategy.num_replicas_in_sync)
 UpperCamelCase			  =      steps_per_epoch * args.num_epochs

 with strategy.scope():
  UpperCamelCase			  =      TFAutoModelForMaskedLM.from_config(UpperCamelCase_       )
  model(model.dummy_inputs       )  # Pass some dummy inputs through the model to ensure all the weights are built
  UpperCamelCase  ,     UpperCamelCase			  =      create_optimizer(
      num_train_steps=UpperCamelCase_							,					num_warmup_steps=total_train_steps // 20							,					init_lr=args.learning_rate							,					weight_decay_rate=args.weight_decay_rate							,					)

  # Transformers models compute the right loss for their task by default when labels are passed, and will
  # use this for training unless you specify your own loss function in compile().
  model.compile(optimizer=UpperCamelCase_							,					metrics=["""accuracy"""]       )

 def decode_fn(UpperCamelCase_       ):
  UpperCamelCase			  =      {
      """input_ids""": tf.io.FixedLenFeature(dtype=tf.intaa							,					shape=(args.max_length,)       ),
      """attention_mask""": tf.io.FixedLenFeature(dtype=tf.intaa							,					shape=(args.max_length,)       ),
  }
  return tf.io.parse_single_example(UpperCamelCase_							,					UpperCamelCase_       )

 # Many of the data collators in Transformers are TF-compilable when return_tensors == "tf", so we can
 # use their methods in our data pipeline.
 UpperCamelCase			  =      DataCollatorForLanguageModeling(
     tokenizer=UpperCamelCase_							,					mlm_probability=args.mlm_probability							,					mlm=UpperCamelCase_							,					return_tensors="""tf"""       )

 def mask_with_collator(UpperCamelCase_       ):
  # TF really needs an isin() function
  UpperCamelCase			  =      (
      ~tf.cast(batch["""attention_mask"""]							,					tf.bool       )
      | (batch["""input_ids"""] == tokenizer.cls_token_id)
      | (batch["""input_ids"""] == tokenizer.sep_token_id)
  )
  UpperCamelCase  ,     UpperCamelCase			  =      data_collator.tf_mask_tokens(
      batch["""input_ids"""]							,					vocab_size=len(UpperCamelCase_       )							,					mask_token_id=tokenizer.mask_token_id							,					special_tokens_mask=UpperCamelCase_							,					)
  return batch

 UpperCamelCase			  =      args.per_replica_batch_size * strategy.num_replicas_in_sync

 UpperCamelCase			  =      prepare_dataset(
     UpperCamelCase_							,					decode_fn=UpperCamelCase_							,					mask_fn=UpperCamelCase_							,					batch_size=UpperCamelCase_							,					shuffle=UpperCamelCase_							,					shuffle_buffer_size=args.shuffle_buffer_size							,					)

 UpperCamelCase			  =      prepare_dataset(
     UpperCamelCase_							,					decode_fn=UpperCamelCase_							,					mask_fn=UpperCamelCase_							,					batch_size=UpperCamelCase_							,					shuffle=UpperCamelCase_							,					)

 UpperCamelCase			  =      []
 if args.hub_model_id:
  callbacks.append(
      PushToHubCallback(output_dir=args.output_dir							,					hub_model_id=args.hub_model_id							,					tokenizer=UpperCamelCase_       )       )

 model.fit(
     UpperCamelCase_							,					validation_data=UpperCamelCase_							,					epochs=args.num_epochs							,					callbacks=UpperCamelCase_							,					)

 model.save_pretrained(args.output_dir       )


if __name__ == "__main__":
     _SCREAMING_SNAKE_CASE 		=      parse_args()
     main(args)

import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )		->	Any:







 '''simple docstring'''
 # Initialise PyTorch model
 UpperCamelCase			  =      MobileBertConfig.from_json_file(UpperCamelCase_       )
 print(f"""Building PyTorch model from configuration: {config}"""       )
 UpperCamelCase			  =      MobileBertForPreTraining(UpperCamelCase_       )
 # Load weights from tf checkpoint
 UpperCamelCase			  =      load_tf_weights_in_mobilebert(UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )
 # Save pytorch-model
 print(f"""Save PyTorch model to {pytorch_dump_path}"""       )
 torch.save(model.state_dict()							,					UpperCamelCase_       )


if __name__ == "__main__":
     _SCREAMING_SNAKE_CASE 		=      argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
     )
     parser.add_argument(
         """--mobilebert_config_file""",
         default=None,
         type=str,
         required=True,
         help=(
             """The config json file corresponding to the pre-trained MobileBERT model. \n"""
             """This specifies the model architecture."""
         ),
     )
     parser.add_argument(
         """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
     )
     _SCREAMING_SNAKE_CASE 		=      parser.parse_args()
     convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)
